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AIM: To examine subfatin, preptin and betatrophin levels in plasma and aqueous in patients with diabetes mellitus (DM) (with and without retinopathy). MATERIAL AND METHOD: Sixty patients, who were similar in terms of age and gender, and were scheduled for operation due to cataract, were included in the study. The patients were divided into three groups as Group C (20 weeks without diabetes and comorbidity), Group DM (20 patients with DM but no retinopathy) and Group DR (20 patients with diabetic retinopathy). The preoperative body mass index (BMI), fasting plasma glucose, HbA1c, lipid profile levels of all patients in the groups were examined. Blood samples were also taken for plasma subfatin, preptin and betatrophin levels. At the beginning of the cataract surgery, 0.1 ml of aqueous fluid was taken from the anterior chamber. Plasma and aqueous subfatin, preptin and betatrophin levels were analyzed by ELISA (enzyme-linked immunosorbent assays) method. RESULTS: In our study results, there was a significant difference in BMI, fasting plasma glucose and hemoglobin A1c levels (p < 0.05 for all parameters). Plasma and aqueous subfatin levels were higher in Group DR compared to Group C (p < 0.001, p = 0.036, respectively). Plasma and aqueous preptin levels were higher in group DR and group DM than in group C (p = 0.001, p = 0.002, p < 0.001, p = 0.001, respectively). Plasma and aqueous betatrophin levels were higher in Group DR compared to group C (p = 0.001, p = 0.010, respectively). CONCLUSION: Subfatin, preptin and betatrophin molecules may have an important role in the pathogenesis of diabetic retinopathy.
Subject(s)
Cataract , Diabetes Mellitus , Diabetic Retinopathy , Retinal Diseases , Humans , Angiopoietin-Like Protein 8 , Blood GlucoseABSTRACT
In this study, the authors aim to investigate the effect of dual antiplatelet agents on peri-implant-guided bone regeneraation by studying a sample of rats with titanium implants in their tibias. The rats were randomly divided into 5 groups: acetylsalicylic acid (ASA) (n=10), treated with 20 mg/kg of ASA; ASA+CLPD (Clopidogrel): (n=10), treated with 20 mg/kg of ASA and 30 mg/kg of clopidogrel; ASA+PRSG (Prasugrel): (n=10), treated with 20 mg/kg of ASA and 15 mg/kg of prasugrel; ASA+TCGR (Ticagrelor): (n=10), treated with 20 mg/kg of ASA and 300 mg/kg of ticagrelor; and a control group (n=10) received no further treatment after implant surgery. Bone defects created half of the implant length circumferencial after implant insertion and defects filled with bone grafts. After 8 weeks experimental period, the rats sacrified and implants with surrounding bone tissues were collected to histologic analysis; bone filling ratios of defects (%) and blood samples collected to biochemical analysis (urea, creatinine, aspartate aminotransferase, alanine aminotransferase, phosphorus, magnesium, alkaline phosphatase, calcium, and parathormone). A statistically significant difference was not detected between the groups for all parameters ( P >0.05). When the percentage of new bone formation was examined, it was found that there was no statistically significant difference between the groups ( P >0.05). Antiplatelet therapy may not adversely affect guided bone regeneration in peri-implant bone defects.
Subject(s)
Dental Implants , Platelet Aggregation Inhibitors , Animals , Rats , Platelet Aggregation Inhibitors/pharmacology , Osseointegration , Clopidogrel , Prasugrel Hydrochloride , Ticagrelor , Bone Regeneration , Aspirin/pharmacologyABSTRACT
Background: The acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease seriously affected worldwide health. It remains an important worldwide concern as the number of patients infected with this virus and the death rate is increasing rapidly. Early diagnosis is very important to hinder the spread of the coronavirus. Therefore, this article is intended to facilitate radiologists automatically determine COVID-19 early on X-ray images. Iterative Neighborhood Component Analysis (INCA) and Iterative ReliefF (IRF) feature selection methods are applied to increase the accuracy of the performance criteria of trained deep Convolutional Neural Networks (CNN). Materials and methods: The COVID-19 dataset consists of a total of 15153 X-ray images for 4961 patient cases. The work includes thirteen different deep CNN model architectures. Normalized data of lung X-ray image for each deep CNN mesh model are analyzed to classify disease status in the category of Normal, Viral Pneumonia and COVID-19. The performance criteria are improved by applying the INCA and IRF feature selection methods to the trained CNN in order to improve the analysis, forecasting results, make a faster and more accurate decision. Results: Thirteen different deep CNN experiments and evaluations are successfully performed based on 80-20% of lung X-ray images for training and testing, respectively. The highest predictive values are seen in the analysis using INCA feature selection in the VGG16 network. The means of performance criteria obtained using the accuracy, sensitivity, F-score, precision, MCC, dice, Jaccard, and specificity are 99.14%, 97.98%, 99.58%, 98.80%, 97.81%, 98.83%, 97.68%, and 99.56%, respectively. This proposed study is indicated the useful application of deep CNN models to classify COVID-19 in X-ray images.
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OBJECTIVES: Atrial fibrillation (AF) is one of the most common causes of preventable ischemic stroke and is related to increased cardiovascular morbidity and mortality. There is a lack of data in Turkey on the use of new oral anticoagulants (NOACs), and time in therapeutic INR range (TTR) in vitamin K antagonist users and AF management modality. In this multi-center trial, we aimed to analyze, follow and evaluate the epidemiological data in non-valvular AF patients. STUDY DESIGN: Four thousand one hundred consecutive adult patients from 42 centers with at least one AF attack identified on electrocardiography will be included in the study. Patients with rheumatic mitral valve stenosis and prosthetic valve disease will be excluded from the study. At the end of one year, the patients will be evaluated in terms of major cardiac end points (death, transient ischemic attack, stroke, systemic thromboembolism, major bleeding and hospitalization). RESULTS: First results are expected in June 2015. Data about major cardiovascular end-points will be available in January 2016. CONCLUSION: The rates and kind of oral anticoagulant use, TTR in vitamin K antagonist users and main management modality applied in non-valvular AF patients will be determined by AFTER-2 study. In addition, the rate of major adverse events (MACEs) and the independent predictors of these MACEs will be detected (AFTER-2 Study ClinicalTrials.gov number, NCT02354456.).
Subject(s)
Anticoagulants/administration & dosage , Atrial Fibrillation/drug therapy , Atrial Fibrillation/epidemiology , Vitamin K/antagonists & inhibitors , Humans , Turkey/epidemiologyABSTRACT
AIM: Endothelial dysfunction and microvascular damage are involved in the pathogenesis of erectile dysfunction (ED). Soluble lectin-like oxidized low-density lipoprotein receptor-1 (sLOX-1) is identified endothelial receptor for oxidized low-density lipoprotein (ox-LDL) that plays a pivotal role in ox-LDL-induced endothelial dysfunction. The purpose of the current study was to determine the association between sLOX-1 and ED in patients without known coronary artery disease (CAD). MAIN OUTCOME MEASURES: Diagnosis of ED was based on the International Index of Erectile Function Score-5. Levels of sLOX-1 were measured in serum by enzyme-linked immunosorbent assay. METHODS: One hundred thirty-eight subjects with ED patients without known CAD (ED group) and 75 age-matched subjects without ED and known CAD (Non-ED Group) were included in this study. RESULTS: Plasma levels of sLOX-1 were significantly higher in ED than in Non-ED group (95±87 and 49±30 pg/mL, respectively, P<0.001). The levels of sLOX-1 highly negative correlated with score of ED (r=-0.618, P<0.001). The sLOX-1 levels>75 pg/mL predicts ED with 26.8% sensitivity and 96.0% specificity on receiver operator characteristic analysis. CONCLUSIONS: Our study demonstrated that serum sLOX-1 levels were associated with endothelial dysfunction that predicts ED. Moreover, the current study revealed that there was strong negative correlation between the levels of circulating sLOX-1 and score of ED. This study suggested sLOX-1 may be involved in the pathogenesis of ED in patients without known CAD.
Subject(s)
Erectile Dysfunction/blood , Scavenger Receptors, Class E/blood , Case-Control Studies , Coronary Artery Disease/blood , Enzyme-Linked Immunosorbent Assay , Humans , Male , Middle Aged , ROC CurveABSTRACT
OBJECTIVE: The purpose of this study was to evaluate the tenascin-C levels in severe rheumatic mitral stenosis before and after percutaneous mitral balloon valvuloplasty (PMBV). SUBJECTS AND METHODS: Forty patients with severe mitral stenosis requiring PMBV and 20 age-matched healthy subjects were included in the study. The mitral valve areas, mitral gradients and systolic pulmonary artery pressure (sPAP) were measured by echocardiography. The sPAP values and mitral gradients were also measured by catheterization before and after PMBV. The blood tenascin-C levels were measured before PMBV and 1 month after the procedure. RESULTS: The echocardiographic mean mitral gradients had a significant decrease after PMBV (11.7 ± 2.8 vs. 5.6 ± 1.7 mm Hg; p < 0.001) and also those of catheterization (13.9 ± 4.4 vs. 4.0 ± 2.4 mm Hg; p < 0.001). Mitral valve areas increased significantly after PMBV (from 1.1 ± 0.1 to 1.8 ± 0.2 cm(2), p < 0.001). Tenascin-C levels decreased significantly in patients after PMBV (from 15.0 ± 3.8 to 10.9 ± 3.1 ng/ml; p < 0.001). Tenascin-C levels were higher in patients with mitral stenosis before PMBV than in healthy subjects (15.0 ± 3.8 and 9.4 ± 2.9 ng/ml; p < 0.001, respectively). There were no significant differences between patients with mitral stenosis after PMBV and healthy subjects (10.9 ± 3.1 and 9.4 ± 2.9 ng/ml; p = 0.09, respectively). There was a significant positive correlation between tenascin-C levels and sPAP (r = 0.508, p < 0.001). In multivariant analysis, tenascin-C predicted mitral stenosis (p = 0.004, OR: 2.31). CONCLUSIONS: Tenascin-C was an independent predictor for rheumatic mitral stenosis.
Subject(s)
Mitral Valve Stenosis/blood , Mitral Valve Stenosis/surgery , Rheumatic Heart Disease/blood , Rheumatic Heart Disease/surgery , Tenascin/blood , Adult , Age Factors , Balloon Valvuloplasty , Biomarkers , Comorbidity , Echocardiography, Doppler , Female , Humans , Male , Middle Aged , Sex FactorsABSTRACT
OBJECTIVE: This study aimed to define the association between altitude and ticagrelor-associated dyspnea in patients with acute coronary syndrome (ACS). METHODS: We studied consecutive patients with de novo ACS who were admitted to two centers at a low altitude (18 and 25 m, n = 65) and two centers at a high altitude (1313 and 1041 m, n = 136). We managed them with ticagrelor between May 2017 and September 2017. Patients with ACS underwent an interventional procedure within <90 minutes in those with ST elevation and within <3 hours in those without ST elevation. We recorded the incidence of dyspnea in patients with ACS receiving ticagrelor therapy. RESULTS: The mean age was 59.5 ± 10 years, and the mean ejection fraction was 43% ± 18%. A total of 110 (56.7%) patients had ST elevation and 84 (43.3%) did not. There were no significant differences in cardiac risk factors, concurrent medications, or procedural variables between the two groups. Dyspnea developed during hospitalization in 53 (38%) patients from high-altitude centers and in 13 (20%) patients from low-altitude centers (66 patients represented 32% of the total ACS cohort). CONCLUSIONS: Dyspnea is a common multifactorial symptom in patients following development of ACS. Ticagrelor-induced dyspnea appears to be associated with altitude.
Subject(s)
Acute Coronary Syndrome , Percutaneous Coronary Intervention , ST Elevation Myocardial Infarction , Humans , Middle Aged , Aged , Ticagrelor/adverse effects , Platelet Aggregation Inhibitors/therapeutic use , Acute Coronary Syndrome/drug therapy , Acute Coronary Syndrome/diagnosis , Altitude , ST Elevation Myocardial Infarction/drug therapy , Dyspnea/drug therapy , Treatment OutcomeABSTRACT
Myocardial infarction (MI) is detected using electrocardiography (ECG) signals. Machine learning (ML) models have been used for automated MI detection on ECG signals. Deep learning models generally yield high classification performance but are computationally intensive. We have developed a novel multilevel hybrid feature extraction-based classification model with low time complexity for MI classification. The study dataset comprising 12-lead ECGs belonging to one healthy and 10 MI classes were downloaded from a public ECG signal databank. The model architecture comprised multilevel hybrid feature extraction, iterative feature selection, classification, and iterative majority voting (IMV). In the hybrid handcrafted feature (HHF) generation phase, both textural and statistical feature extraction functions were used to extract features from ECG beats but only at a low level. A new pooling-based multilevel decomposition model was presented to enable them to create features at a high level. This model used average and maximum pooling to create decomposed signals. Using these pooling functions, an unbalanced tree was obtained. Therefore, this model was named multilevel unbalanced pooling tree transformation (MUPTT). On the feature extraction side, two extractors (functions) were used to generate both statistical and textural features. To generate statistical features, 20 commonly used moments were used. A new, improved symmetric binary pattern function was proposed to generate textural features. Both feature extractors were applied to the original MI signal and the decomposed signals generated by the MUPTT. The most valuable features from among the extracted feature vectors were selected using iterative neighborhood component analysis (INCA). In the classification phase, a one-dimensional nearest neighbor classifier with ten-fold cross-validation was used to obtain lead-wise results. The computed lead-wise results derived from all 12 leads of the same beat were input to the IMV algorithm to generate ten voted results. The most representative was chosen using a greedy technique to calculate the overall classification performance of the model. The HHF-MUPTT-based ECG beat classification model attained excellent performance, with the best lead-wise accuracy of 99.85% observed in Lead III and 99.94% classification accuracy using the IMV algorithm. The results confirmed the high MI classification ability of the presented computationally lightweight HHF-MUPTT-based model.
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PROBLEM: Cough-based disease detection is a hot research topic for machine learning, and much research has been published on the automatic detection of Covid-19. However, these studies are useful for the diagnosis of different diseases. AIM: In this work, we collected a new and large (n=642 subjects) cough sound dataset comprising four diagnostic categories: 'Covid-19', 'heart failure', 'acute asthma', and 'healthy', and used it to train, validate, and test a novel model designed for automatic detection. METHOD: The model consists of four main components: novel feature generation based on a specifically directed knight pattern (DKP), signal decomposition using four pooling methods, feature selection using iterative neighborhood analysis (INCA), and classification using the k-nearest neighbor (kNN) classifier with ten-fold cross-validation. Multilevel multiple pooling decomposition combined with DKP yielded 41 feature vectors (40 extracted plus one original cough sound). From these, the ten best feature vectors were selected. Based on each vector's misclassification rate, redundant feature vectors were eliminated and then merged. The merged vector's most informative features automatically selected using INCA were input to a standard kNN classifier. RESULTS: The model, called DKPNet41, attained a high accuracy of 99.39% for cough sound-based multiclass classification of the four categories. CONCLUSIONS: The results obtained in the study showed that the DKPNet41 model automatically and efficiently classifies cough sounds for disease diagnosis.
Subject(s)
Asthma , COVID-19 , Humans , Cough/diagnosis , Asthma/diagnosis , Machine Learning , Support Vector MachineABSTRACT
BACKGROUND AND PURPOSE: Valvular heart disease (VHD) is an important cause of morbidity and mortality. Echocardiography is the reference standard for VHD diagnosis but is not universally accessible. Manual cardiac auscultation is inadequate for screening VHD. Many machine learning models using heart sounds acquired with an electronic stethoscope may improve the accuracy of VHD diagnosis. We aimed to develop an accurate sound classification model for VHD diagnosis. MATERIALS AND METHODS: A new large stethoscope sound dataset containing 10,366 heart sounds divided into ten categories (nine VHD and one healthy) were prospectively collected. We developed a handcrafted learning model that comprised multilevel feature extraction based on a dual symmetric tree pattern (DSTP) and multilevel discrete wavelet transform (DWT), feature selection, and classification. The multilevel DWT was used to create subbands to extract features at both high and low levels. Then, iterative neighborhood component analysis was used to select the most discriminative 512 features from among the extracted features in the generated feature vector. In the classification phase, a support vector machine (SVM) was used with 10-fold cross-validation (CV) and leave-one-subject-out (LOSO) CV. RESULTS: Our proposed DSTP-based model attained 99.58% and 99.84% classification accuracies using SVM classifier with 10-fold CV and LOSO CV, respectively. CONCLUSIONS: The presented DSTP-based classification model attained excellent multiclass classification performance on a large prospective heart sound dataset at a low computational cost.
Subject(s)
Heart Valve Diseases , Models, Theoretical , Decision Trees , Heart Sounds , Heart Valve Diseases/diagnosis , Humans , Prospective Studies , Reproducibility of Results , Stethoscopes , Support Vector MachineABSTRACT
BACKGROUND AND PURPOSE: Biometrics is a commonly studied research issue for both biomedical engineering and forensics sciences. Besides, the purpose of hidden biometrics is to discover hidden biometrics features. This work aims to demonstrate the biometric identification ability of coronary angiography images. MATERIAL AND METHOD: A new coronary angiography images database was collected to develop an automatic identification model. The used database was collected from 51 subjects and contains 2156 images. The developed model has to preprocess; feature generation using local binary pattern; feature selection with neighborhood component analysis; and classification phases. In the preprocessing phase; image rotations; median filter; Gaussian filter; and speckle noise addition functions have been used to generate filtered images. A multileveled extractor is presented using local binary pattern and maximum pooling together. The generated features are fed to neighborhood component analysis and the selected features are classified using k nearest neighbor classifier. RESULTS: The presented angiography image identification method attained 99.86% classification accuracy on the collected database. CONCLUSIONS: The obtained findings demonstrate that the angiography images can be utilized as biometric identification. Moreover, we discover a new hidden biometric feature using coronary angiography images and name of this hidden biometric is coronary angiography print.
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BACKGROUND: Index of cardio-electrophysiological balance (iCEB) has been described as a novel risk marker for predicting malignant ventricular arrhythmia. There remains limited evidence on the effects of amiodarone and propafenone used for sinus rhythm maintenance on iCEB in patients with atrial fibrillation (AF). The aim of this study was to evaluate iCEB in patients with AF on antiarrhythmic-drug therapy. METHODS: A total of 108 patients with AF (68 patients using amiodarone and 40 patients using propafenone) and 50 healthy subjects were included in the study. All groups underwent a standard 12-lead surface electrocardiogram. QRS duration, QT, T wave peak-to-end (Tp-e) intervals, iCEB (QT/QRS) and iCEBc (heart rate-corrected QT (QTc)/QRS) rates were calculated from the electrocardiogram and compared between groups. RESULTS: QT, Tp-e intervals and Tp-e/QT ratio were significantly longer in the amiodarone group than the propafenone and control groups (P < 0.001, for all). iCEB was similar in the amiodarone and control groups (4.4 ± 0.6 and 4.2 ± 0.4; P > 0.05), while iCEB values in the propafenone group were significantly lower than the amiodarone group and control groups (3.9 ± 0.5; P < 0.001). There was a significantly difference in iCEBc values among the amiodarone, control and propafenone groups (4.8 ± 0.6, 4.6 ± 0.4 and 4.3 ± 0.6; P < 0.001, respectively). CONCLUSIONS: In this study, higher iCEBc parameters were observed in patients using amiodarone, while iCEBc values were lowest among patients with AF using propafenone. Further studies are needed to determine whether these electrophysiological changes are associated with ventricular arrhythmias for patients with AF on antiarrhythmic-drug therapy.
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[This corrects the article DOI: 10.14740/cr1185.].
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COVID-19 and heart failure (HF) are common disorders and although they share some similar symptoms, they require different treatments. Accurate diagnosis of these disorders is crucial for disease management, including patient isolation to curb infection spread of COVID-19. In this work, we aim to develop a computer-aided diagnostic system that can accurately differentiate these three classes (normal, COVID-19 and HF) using cough sounds. A novel handcrafted model was used to classify COVID-19 vs. healthy (Case 1), HF vs. healthy (Case 2) and COVID-19 vs. HF vs. healthy (Case 3) automatically using deoxyribonucleic acid (DNA) patterns. The model was developed using the cough sounds collected from 241 COVID-19 patients, 244 HF patients, and 247 healthy subjects using a hand phone. To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals. Our proposed model comprises a graph-based local feature generator (DNA pattern), an iterative maximum relevance minimum redundancy (ImRMR) iterative feature selector, with classification using the k-nearest neighbor classifier. Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively. The developed system is completely automated and economical, and can be utilized to accurately detect COVID-19 versus HF using cough sounds.
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OBJECTIVES: The aim of this experimental animal study was to evaluate the effects of systemic propranolol on new bone formation in peri-implant bone defects. MATERIAL AND METHODS: Implant slots were created 4mm long and 2.5 mm wide. After the titanium implants were placed in the sockets, 2 mm defects were created in the neck of the implants. Bone grafts were placed in these defects. Then the rats were randomly divided into three equal groups: control (n = 8), propranolol dose-1 (PRP-1) (n = 8), and propranolol dose-2 (PRP-2) (n = 8) groups. In the control group, the rats received no further treatment during the eight-week experimental period after the surgery. The rats in the PRP-1 and PRP-2 groups were given 5 mg/kg and 10 mg/kg propranolol, respectively, every three days for the eight-week experimental period after the surgery. At the end of the experimental period, the rats were euthanized. Blood serum was collected for biochemical analysis, and the implants and surrounding bone tissues were used for the histological analysis. RESULTS: There were no significant differences in the histological analysis results and the biochemical parameters (alkaline phosphatase, calcium, creatinine and phosphorus) of the groups (P > 0.05). Also, in the test groups, there was numerically but not statistically more new bone formation detected compared with the controls. CONCLUSIONS: Within the limitations of this study, propranolol did not affect the new bone formation in peri-implant defects.
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INTRODUCTION: Migraine, the most widespread cause of headache in young adults, is a frequent reason for presentation to emergency departments. The aim of this study was to determine the diagnostic value of serum galectin-3 and high-sensitivity C-reactive protein (hsCRP) levels in migraine patients. MATERIAL AND METHODS: Serum galectin-3 and hsCRP levels were measured using enzyme-linked immunosorbent assay in 70 migraine patients and 70 healthy control subjects. RESULTS: The mean values of hsCRP and galectin-3 levels were significantly higher in the migraine group than those in the control group (1.67 ±0.49, 1.12 ±0.38 mg/l, p < 0.001; and 3.76 ±1.45, 2.05 ±0.29 ng/ml, p < 0.001, respectively). When the diagnostic values of the biomarkers were assessed in the migraine attack and control groups, the sensitivity and specificity of the test for hsCRP at the cut-off point of 1.32 mg/l were 70% and 73% (AUC = 0.805, 95% CI: 0.74-0.88, p < 0.001), and for galectin-3 at the cut-off point of 2.4 ng/ml, 89% and 90% (AUC = 0.97, 95% CI: 0.95-0.99, p < 0.001), respectively. CONCLUSIONS: This study showed the presence of a relation between high serum levels of hsCRP and galectin-3 in migraine patients. However, well-controlled, carefully executed longitudinal studies are required to confirm these findings.
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Pericardial effusion is common disease and difficult to diagnose. Tuberculosis accounts for up to 4% of acute pericarditis and 7% of cardiac tamponade cases. Quick treatment can be lifesaving but requires accurate diagnosis. We report a case of a 65-year-old man who presented with a 3-week history of fever with chills, non-productive cough and dyspnea. The case was diagnosed by positivity of acid-fast staining, culture and polymerase chain reaction (PCR) of the aspirated pericardial fluid and treated promptly with antituberculosis drugs. The patient showed complete recovery.
Subject(s)
Antitubercular Agents/therapeutic use , Ethambutol/therapeutic use , Isoniazid/therapeutic use , Pericarditis, Tuberculous/diagnosis , Pericarditis, Tuberculous/drug therapy , Rifampin/therapeutic use , Tuberculin Test , Aged , Humans , Male , Pyrazinamide/therapeutic use , Treatment Outcome , Tuberculin Test/methodsABSTRACT
OBJECTIVE: The awareness, time in therapeutic range (TTR), and safety of warfarin therapy were investigated in the adult Turkish population. METHODS: This multicenter prospective study includes 4987 patients using warfarin and involved regular international normalized ratio (INR) monitoring between January 1, 2014 and December 31, 2014. TTR was calculated according to F.R. Roosendaal's algorithm. Awareness was evaluated based on the patients' knowledge of warfarin's affect and food-drug interactions. RESULTS: The mean TTR of patients was 49.52±22.93%. The patients with hypertension (55.3%), coronary artery disease (23.2%), congestive heart failure (24.5%), or smoking habit (20.8%) had significantly lower TTR levels than the others. Of the total number of patients, 42.6% had a mechanical valve, 38.4% had non-valvular atrial fibrillation (AF), and 19% had other indications for warfarin. Patients with other indications had lower TTR levels than those with mechanical valve and non-valvular AF (p=0.018). Warfarin awareness decreased in higher age groups. The knowledge of warfarin's food-drug interactions was 55%. People with higher warfarin awareness had higher TTR levels. Patients with ≤8 INR monitoring/year had lower TTR levels (46.4±25.3 vs. 51.1±21.3, respectively, p<0.001) and lower awareness (44.6% vs. 60.6%, p<0.001) than patients with ≥8 INR monitoring/year. In this study, 20.1% of the patients had a bleeding event (major bleeding 15.8%, minor bleeding 84.2%) within a year. CONCLUSION: Both the mean TTR ratios and awareness of the Turkish population on warfarin therapy were found to be low. It was thought that low TTR levels of the Turkish population may be caused by the low awareness of warfarin, warfarin's food-drug interactions, and high rates of concomitant diseases.
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AIM: To evaluate QT dispersion (QTD) in patients with central serous chorioretinopathy (CSC). METHODS: This clinical, comperative, case-control study included 30 patients with CSC at acute phase (Group 1) and 30 age- and sex-matched healthy subjects (Group 2, the control group). From all subjects, a 12-lead surface electrocardiography was obtained. The heart rate (HR), QT maximum (QTmax), QT minimum (QTmin), QT corrected (QTc), QTD and Tmean were manually measured and analyzed. Student's t-test and Pearson's method of correlation were used for statistical analysis. RESULTS: The patient and control groups were matched for age, smoking status (rate and duration) and gender. There were no significant differences with regard to these among the groups (P>0.05). The participants included 19 men (63.3%) and 11 women (36.7%) in Group 1, 20 men (66.7%) and 10 women (33.3%) in Group 2. QTmax, QTD and QTc were significantly higher than those of healthy controls (P<0.001 for QTmax, P=0.01 for QTD and P=0.001 for QTc). QTmin, Tmean and HR did not differ significantly between the study groups (P=0.28 for QTmin, P=0.56 for Tmean and P>0.05 for HR). No significant correlation was found between duration of the disorder and QTD values (r=0.13, P>0.05). CONCLUSION: These findings suggest that CSC may be associated with an increase in QTD and that the patients might be at risk for ventricular arrhythmia.
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OBJECTIVES: The purpose of this study was to determine whether admission soluble glycoprotein VI (sGP-VI) level is associated with no-reflow phenomenon (NRP) after primary percutaneous coronary intervention (P-PCI) in patients with acute ST-segment elevation myocardial infarction (STEMI). METHODS: A total of 178 consecutive patients admitted to our hospital for a first STEMI and undergoing P-PCI within 12 hours of onset of symptoms were enrolled. The patients were divided into 2 groups (NRP group and reflow group). Admission sGP-VI plasma levels were measured by enzyme-linked immunosorbent assay. RESULTS: Of the 178 patients who underwent P-PCI, 41 patients (23%) developed NRP. The patients in the reflow group had higher levels of sGP-VI compared with the patients in the NRP group (38.5 ± 21.0 vs 21.9 ± 11.9 ng/mL, P < 0.001). The sensitivity and specificity values of the sGP-VI levels were 90% and 49%, respectively (cutoff value was ≤ 25). In the multivariate logistic regression analyses, sGP-VI levels of 25 ng/mL or lower, higher peak troponin T levels and body mass index value, amount of opaque of greater than 250 mL, and lesion length of greater than 13.5 mm were independent predictors of angiographic NRP. CONCLUSIONS: Lower admission sGP-VI levels are associated with NRP in patients with STEMI undergoing P-PCI. This outcome may open new therapeutic facility in the setting of P-PCI.